Caching Stars in the Sky: A Semantic Caching Approach to Accelerate Skyline Queries

TL;DR

This paper introduces a semantic caching approach that leverages previous skyline queries to significantly speed up multi-criteria decision making in high-dimensional, online datasets, enabling real-time applications.

Contribution

It proposes a novel caching mechanism and index structure that utilize query semantics to accelerate skyline query processing, including related and partial results.

Findings

Effective acceleration of skyline queries demonstrated

Scalability confirmed on synthetic and real datasets

Improved processing times for high-dimensional data

Abstract

Multi-criteria decision making has been made possible with the advent of skyline queries. However, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially for non-indexed skyline techniques where the datasets arrive online. In this paper, we propose a caching mechanism that uses the semantics of previous skyline queries to improve the processing time of a new query. In addition to exact queries, utilizing such special semantics allow accelerating related queries. We achieve this by generating partial result sets guaranteed to be in the skyline sets. We also propose an index structure for efficient organization of the cached queries. Experiments on synthetic and real datasets show the effectiveness and scalability of our proposed methods.